Which Database Should I Use?

database

Databases fulfill a vital role in our businesses. They allow us to maintain, update, and edit our stored information quickly and securely. We use databases for a wide variety of data storage needs like inventory tracking, customer relationship management, payroll, data analysis, networking, and much more. Doing this efficiently means we can support the wider business functions and help the company excel at its goals.

The key to managing your stored data efficiently lies in which database you use. Not all databases are built the same, and some databases are better for some tasks than others. Here we’re going to look at the most popular databases out there and assess the pros and cons for each. Let’s take a look.

Relational Databases (SQL)

Put simply, relational databases store and provide access to data points that are related to each other. SQL is the primary language of relational databases.

The items within the database have a predefined relationship between them and are typically organized as a set of tables complete with columns and rows. A basic example of an SQL database goes something like this. You have a table for weather data. The Columns feature the days of the week. The rows feature the time of the day. And the datapoint is degrees celsius.

Common examples of relational databases include MySQL, MS Server, Oracle, IBM DB2, and PostgreSQL.

Pros of Relational Databases

  • SQL is widely known and understood, and there are many stable and standardized relational databases available.
  • The costs and risks of relational database development are well understood. This makes embarking on relational database projects straightforward.
  • The fundamental structure of the database is easy to understand.

Cons of Relational Databases

  • They lack support when it comes to complex data types like drawings. SQL is relatively limited when it comes to accessing complex data.

Non-Relational Databases (NoSQL)

A non-relational database stores data, but there are no tables, columns, or rows like in relational databases. NoSQL excels at handling data that isn’t structured enough to require a relational database but still needs to be stored. For example, Facebook Messenger uses a NoSQL database because the data it collects isn’t structured enough to be segmented into tables and define relationships between the data. Some common NoSQL databases are Cassandra, MongoDB, Apache HBase, Redis, and Amazon DynamoDB.

Pros of Non-Relational Databases

  • Flexibility – Perhaps the biggest pro of NoSQL databases is that they are highly flexible. While relational databases can be difficult to scale when it comes to commodity clusters, NoSQL doesn’t suffer from this problem. We live in a world where outward scalability is outpacing forward scalability, and NoSQL shines in this area, so it is often preferred.
  • Huge data stores – NoSQL databases can store vast amounts of data. While relational databases can store vast amounts of data, they don’t handle it as easily as NoSQL databases.
  • Maintenance -NoSQL databases are typically easier to maintain than complex relational databases, which often require a dedicated expert.
  • Cost – NoSQL databases are cheap and easy (not labor intensive) to install.

Cons of Non-Relational Databases

  • Not as mature as relational databases – Relational databases have been around longer, so we have a well-defined blueprint for making them stable and highly functional.
  • Support is less comprehensive – More suppliers of relational databases offer 24/7 support. This can be true for NoSQL databases too, but not always.
  • Analytics and intelligence -NoSQL databases were built with modern web 2.0 web applications in mind and are competent at providing analytics and intelligence for data associated with these apps. However, analytics and intelligence are often lacking for apps that don’t fall into this category.

Document Databases (Document Stores)

Document databases have a simple table structure with documents to store information. Document stores are used to manage semi-structured data and are often considered a subcategory of NoSQL databases. You might be wondering why you would use a document database over a relational database. The answer lies in the nature of the data. If your data isn’t relational, or there is no easy way to describe it as a set of columns, then a document database is more appropriate.

For example, let’s say you need a database to store paper documents such as office mail, which you convert into a PDF. There will be metadata in the PDF, things like the time it was scanned, who it was scanned by, etc. But the metadata might vary widely between documents. Sometimes it will also include things like the order number, supplier number, customer number, and so on, but not always. You also might not know what metadata will be captured in the future. In this scenario, it makes more sense to use a document database because, with a relational database, you will always be tweaking it to get it to work with the new data you put in it.

Pros Document Stores

  • Preferred for semi-structured data that might change in the future.
  • Flexibility – The structure doesn’t have to be consistent.
  • It’s easy to integrate new information.

Cons of Document Stores

  • They fall apart if you try to use relational elements – If you try to connect documents, it can become extremely complex and challenging to manage.

In-Memory Databases (IMDB)

An in-memory database utilizes the computer’s main memory instead of a disk drive. This allows for quick response times because it eliminates the time it takes to query data from a disk. IMDBs are typically used for real-time banking, online interactive gaming, GIS processing, machine learning, and medical device analytics. The main pro of IMDBs is that they are extremely fast, which is why they are so heavily used in the data analytics industry. However, the downside of these databases is the volatility of RAM (memory). If the in-memory database crashes, then the data is lost.

Streaming Databases

A streaming database is a data store designed to collect, process, and enrich incoming data in real-time. In contrast to the others on the list, a streaming database is not its own database class. Instead, it refers to a way that databases can handle data in real-time. For example, im-memory or NoSQL databases could also utilize database streaming. Streaming databases are excellent at analyzing data in real-time, responding to events quickly, and supporting preventative maintenance. However, if there’s no urgency or time-sensitivity to the data you collect, then database streaming is not needed.

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